Abstract: Diagnosing prostate cancer typically involves invasive measures such as biopsy in which tissue is removed and graded by pathologists. Non-invasive measures like multi-parametric MRI can be used to examine and grade lesions using the PI-RADS scale; however, the achievable image resolution is motion-limited. A new paradigm, magnetic resonance histopathology (MRH), is disclosed which aims to assess tissue texture at sub-millimeter resolution through rapid clinical acquisition procedures. MRH capability of identifying cancerous tissue in the prostate via an in silico analysis is demonstrated, recreating the MRH measurement process on a set of high resolution, pathologist-annotated histology slides. A dataset of spectral intensities at sub-millimeter wavelengths is created and a deep learning model trained to classify spectral data is underlying normal or tumor tissue. A set of spatial frequencies to optimize diagnostic power within the constraint of limited acquisition time is identified.